جست‌وجوی استاد و پژوهشگر

• Computational Neuroscience
• Statistics
• Machine Learning
• +۴ مورد دیگر

Robert E. Kass is the Maurice Falk University Professor of Statistics and Computational Neuroscience at Carnegie Mellon University, with affiliations in the Department of Statistics & Data Science, Machine Learning Department, and Neuroscience Institute. His career spans statistics, machine learning, and neuroscience, focusing on statistical methods for analyzing neural data, particularly spike trains and brain connectivity. Research highlights include: Developing statistical frameworks for neural data analysis Investigating neural oscillations and cross-regional interactions Contributing to neuroscience education through Neuromatch Academy Exploring intersections between statistics, machine learning, and scientific inference Scientific awards and recognitions include: Outstanding Statistical Application Award (ASA) Distinguished Achievement Award (COPSS, 2017) Election to National Academy of Sciences (2023) Fellowships in ASA, IMS, AAAS His recent publications focus on: Neural circuit oscillations and phase-amplitude analysis Latent dynamic modeling of high-dimensional neural recordings Population-level neural interactions and connectivity Educational frameworks for computational neuroscience training Methodological bridges between statistics and machine learning Advanced graphical models for neural synchronization studies

Nicole Fengمشاهده پروفایل

پژوهشگر ارشد

• Computer Graphics
• Geometry Processing
• Computational Geometry
• +۲ مورد دیگر

Nicole Feng is a fifth-year PhD candidate and Research Fellow in Computer Science at Carnegie Mellon University, where she works in the Geometry Collective under Professor Keenan Crane. Her research focuses on geometric algorithms for computer graphics and geometry processing. Feng develops fundamental methods for geometric computing including novel approaches to signed distance calculation and winding number computation on complex surfaces. Her work enables practical applications in surface modeling, scientific visualization, and digital geometry processing. Recognized as a WiGRAPH Rising Star in 2024, she creates educational resources including Blender tutorials for scientific visualization. Beyond research, Feng designs crossword puzzles and organizes community running events.

Mark Stehlikمشاهده پروفایل

استاد آموزشی

• Computer Science Education
• K-12 Outreach
• Programming Pedagogy
• +۳ مورد دیگر

Mark Stehlik is a University Teaching Professor, Assistant Dean for Outreach, and Director of the CSD Undergraduate Program at Carnegie Mellon University's School of Computer Science (SCS). He holds dual affiliations with the Computer Science Department and the SCS Dean's Office. His administrative support is provided by Amy Weis. Stehlik's research focuses on computer science education, K-12 outreach, and curriculum design. Notable projects include the CMU CS Academy, a browser-based programming platform for high school education, and initiatives to address the lack of K-12 CS education in the digital age. He has published extensively on topics like text-based programming, cognitive load in education, and international outreach in Qatar. Recent publications emphasize bridging early college success through programming education and addressing cognitive challenges in curriculum design. His work spans over four decades, with foundational contributions to introductory programming pedagogy via tools like Karel J Robot. Stehlik's teaching roles include courses such as 15-090, 15-111, and 15-594, reflecting his commitment to undergraduate education. His outreach efforts aim to broaden participation in computing through innovative programs and policy advocacy.

Olga Gutanمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Geometry Processing
• Algorithm Design
• +۱ مورد دیگر

Olga Gutan is a doctoral student at Carnegie Mellon University 's Computer Science Department , affiliated with the Geometry Collective and advised by Keenan Crane . Her work focuses on computer graphics and geometry processing , with a primary research interest in vectorization and surface algorithm development. Current role: Doctoral Research Assistant Past work: Nonmanifold Minimal Surfaces with mentors Etienne Vouga, Nicholas Sharp, Josh Vekhter Her research bridges theoretical geometry and practical implementation, with publications on singularity-free frame fields for vectorization and exploratory work on gravitational surface rendering in Houdini. She received an Honorable Mention for Best Paper at Symposium on Geometry Processing 2023. Olga contributes to algorithm development and visualization techniques, including triply-periodic nonmanifold surfaces. Scientific Awards : Best Paper (Honorable Mention), Symposium on Geometry Processing 2023 Key Collaborations : Mentored by Etienne Vouga, Nicholas Sharp, and Josh Vekhter during summer 2021. Affiliated with the Geometry Collective at Carnegie Mellon.

Farnam Jahanianمشاهده پروفایل

استاد

• Computer Animation
• Robotics
• Dynamic Systems Control
• +۲ مورد دیگر

Farnam Jahanian is President and Professor of Computer Science and Robotics at Carnegie Mellon University. His research develops algorithms for controlling dynamic physical systems in robotics and animation, including human motion synthesis, robotic manipulation, and simulated clothing/fluid dynamics. Current work explores motion planning for humanoid robots using motion capture data and physical simulations. Recent publications (2024-2025) focus on kinematic motion retargeting, volumetric hairstyle capture, synthetic data for action recognition, and facial expression translation for robots. His lab integrates computer graphics with robotics to create realistic virtual characters and adaptive control systems.

Pradeep Ravikumarمشاهده پروفایل

استاد

• Machine Learning
• Statistical Learning
• Probabilistic Graphical Models
• +۵ مورد دیگر

Pradeep Ravikumar is a Professor in the Machine Learning Department at Carnegie Mellon University's School of Computer Science. He leads the Statistical & Symbolic Learning (Neuro-Symbolic AI) Group and serves as co-Editor-in-Chief of the Journal of Machine Learning Research (JMLR). Previously, he was Associate Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). His research focuses on machine learning theory, probabilistic graphical models, neuro-symbolic AI, and causal representation learning. He has taught advanced courses such as Advanced Machine Learning (PhD level), Probabilistic Graphical Models, and Convex Optimization. His awards include the Sloan Research Fellowship, NSF CAREER Award, and Siebel Scholarship. His work emphasizes theoretical foundations and practical applications, with recent contributions in neuro-symbolic systems, causal inference, and robust machine learning. Key research themes include differentiable structure learning, interpretable AI, and invariant representation learning. He has published extensively in top venues like NeurIPS, ICML, and JMLR, addressing challenges in optimization, causal discovery, and model interpretability.

Ankur Parikhمشاهده پروفایل

استادیار مدعو

• Natural Language Processing
• Machine Learning
• Spectral Learning
• +۳ مورد دیگر

Ankur Parikh is a staff research scientist at Google NYC and an adjunct assistant professor at New York University. His research focuses on natural language processing, machine learning, and spectral learning methods with applications to graphical models and biological networks. He holds a PhD from Carnegie Mellon University’s Machine Learning Department (2014), advised by Eric Xing, and an undergraduate degree in Computer Science and Applied Math from Princeton University. His work bridges theoretical advancements in machine learning with practical applications, including decomposable attention models for NLP, kernel graphical models, and network analysis of biological systems. Notable contributions include the TREEGL algorithm for gene network inference and spectral methods for latent structure discovery. He has been recognized with best paper awards at EMNLP and ISMB conferences. Education: PhD, Machine Learning, Carnegie Mellon University (2014) BS, Computer Science & Applied Math, Princeton University Parikh has taught advanced courses on spectral learning and probabilistic graphical models, including guest lectures at CMU and tutorials at Microsoft Research. His research is supported by an NSF Graduate Fellowship and has led to open-source implementations like the PLRE language modeling toolkit. Current affiliations include NYU’s machine learning community and Google’s NYC research team, with ongoing collaborations in NLP, computational biology, and statistical network analysis.

Juney Leeمشاهده پروفایل

استادیار

• Sustainable Construction
• Computational Structural Design
• Regenerative Materials
• +۳ مورد دیگر

Juney Lee is the T. David Fitz-Gibbon Assistant Professor of Architecture at Carnegie Mellon University's School of Architecture, appointed in 2023. He leads the Regenerative Structures Laboratory and teaches core structural design courses, emphasizing computational methods and sustainable practices. His research focuses on reducing embodied energy in buildings through advanced materials and construction technologies, with notable projects like the MycoTree and the HiLo unit at NEST. Lee holds degrees from UC Berkeley, MIT (M.Eng. in Structural Engineering), and a Ph.D. from ETH Zurich, where his work earned the Silver Medal. He is a co-developer of the open-source COMPAS framework and RhinoVAULT 2 software, advancing democratized access to computational tools. Education: B.A. in Architecture, University of California, Berkeley M. Arch., Massachusetts Institute of Technology (MIT) M. Eng. (Structural), MIT's Department of Civil and Environmental Engineering Ph.D., Institute of Technology in Architecture at ETH Zurich Research Interests: Lee's work bridges computational design, sustainable materials, and structural innovation. He explores 3D graphic statics, mycelium-based composites, and scalable decarbonization strategies. His teaching integrates hands-on methods to enhance students' design intuition amid AI-driven tools. Awards: Marvin E. Goody Award (MIT, 2014) ETH Zurich Silver Medal (2019) Teaching & Grants: Leads courses in structural design and computational analysis. Advises across all academic levels. Collaborates on projects like NEST's HiLo unit and the Seoul Biennale MycoTree installation. Active in industry-academia translation through prototypes showcased at international biennales. Labs/Teams: Directs the Regenerative Structures Laboratory at CMU, focusing on open-source tools and sustainable construction innovation.

Conrad Tuckerمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Robotics
• +۶ مورد دیگر

Conrad Tucker is a Professor of Mechanical Engineering at Carnegie Mellon University (CMU) and Director of CMU-Africa, with courtesy appointments in Machine Learning, Robotics, and Biomedical Engineering. He serves as Associate Dean for International Affairs (Africa) and holds an endowed Trustee Professorship. His research focuses on AI-driven systems design, data mining, cybersecurity, and education innovation, with grants from NSF, DARPA, and the Gates Foundation. Education: Ph.D. in Industrial & Systems Engineering, University of Illinois, 2011 MBA in Business Administration, University of Illinois, 2009 M.S. in Industrial & Systems Engineering, University of Illinois, 2007 B.S. in Mechanical Engineering, Rose-Hulman Institute of Technology, 2004 Research Interests: Dr. Tucker’s work bridges AI, engineering design, and societal impact. Key areas include: AI/ML for security and privacy Optimization of additive manufacturing AI applications in healthcare and education Data-driven product innovation His lab, AiPEX, develops ML methods for personalized design solutions and addresses challenges like deepfake detection in education. Grants & Awards: Endowed Trustee Professorship (CMU, 2023) NAE Frontiers of Engineering Education Advisory Committee (2016–present) Over $10M in grants from NSF, AFOSR, DARPA, and others Labs & Collaborations: Leads the AiPEX Lab, advancing AI in product design and cybersecurity. Collaborates with institutions globally, including the African Engineering and Technology Network (Afretec), to promote STEM education and digital infrastructure in Africa.

Christopher Genoveseمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Nonparametric Inference
• Computational Neuroscience
• +۷ مورد دیگر

Christopher Genovese serves as Professor and Department Head of the Department of Statistics within Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His academic leadership extends to interdisciplinary collaborations through the Neuroscience Institute, where he contributes to computational neuroscience research initiatives. Dr. Genovese's research spans high-dimensional and nonparametric statistical methodology with applications across multiple scientific domains. His primary focus areas include computational neuroscience, cosmology, evolutionary biology, and educational data science. Specific methodological interests encompass graphical models, spatial statistics, inverse problems, multiple testing procedures, and adaptive function estimation. His recent publication portfolio demonstrates strong interdisciplinary engagement, with computational neuroscience representing the dominant application area (35% of recent work), followed by cosmology (20%), educational technology (15%), and evolutionary biology (10%). Methodologically, high-dimensional statistics and nonparametric inference form the core theoretical contributions across these applications. Current research initiatives include developing systems for inferring student learning states from online educational data and novel methods for predicting placental and fetal health in collaboration with Magee-Womens Hospital researchers. These projects exemplify his commitment to translating statistical theory into practical scientific and medical applications.

Chris Genoveseمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Nonparametric Inference
• Graphical Models & Networks
• +۹ مورد دیگر

Chris Genovese serves as Professor and Chair of the Department of Statistics at Carnegie Mellon University within the Dietrich College of Humanities and Social Sciences. He earned his Ph.D. in statistics from the University of California, Berkeley in 1994 and has remained at Carnegie Mellon since then, rising to leadership of the department. His research program spans theoretical and applied statistics with emphasis on complex scientific problems: High-dimensional statistical methods Nonparametric inference techniques Graphical models and network analysis Computational neuroscience applications Cosmology and astronomical data analysis Genovese maintains active interdisciplinary collaborations across neuroscience, cosmology, astronomy, and evolutionary biology. His theoretical work focuses on confidence sets for nonparametric inference, adaptive function estimation, spatial statistics, inverse problems, and multiple testing procedures. Current projects include developing systems for inferring student learning states from online educational data and creating novel methods for predicting placental and fetal health in collaboration with Magee-Womens Hospital researchers. His research demonstrates the power of statistical methods to address challenging problems across diverse scientific domains.

George A. Kantorمشاهده پروفایل

استاد پژوهشی

• Robotics
• Control Systems
• State Estimation
• +۵ مورد دیگر

George A. Kantor is a Research Professor and Associate Director of Education at Carnegie Mellon University's Robotics Institute, based in the Collaborative Innovation Center (LL050). He leads the Kantor Lab and the AIIRA (AI Institute for Resilient Agriculture), focusing on controlling robotic systems with complex dynamics and state estimation through indirect measurements for applications in agriculture, underwater environments, and mining. His research spans agricultural robotics, field robotics, and control theory, emphasizing dynamics, kinematics, motion planning, and sensing/perception. Key areas include Robotics in Agriculture and Forestry, Underwater Robotics, Mining Robotics, and Sensing & Perception. He combines mathematical modeling with experimental validation to develop real-world solutions for balancing unstable robots and localizing autonomous vehicles in complex environments. Recent publications (2023-2025) reveal a dominant focus on agricultural robotics, featuring computer vision for crop monitoring, autonomous harvesting, and environmental sensing. Trends include transformer networks for fruit tracking, sim2real transfer techniques using Gaussian splatting, audio-visual contact classification, and LiDAR-based navigation in crop fields, demonstrating strong integration of deep learning with agricultural automation. No scientific awards were listed in the available information. Dr. Kantor actively mentors 4 current PhD students (Dominic Guri, John Kim, Mark Lee, Jenny Wang) and 2 master's students (Morgan Mayborne, Mohammad Nomaan Qureshi), with a track record of guiding 1 PhD graduate and over 20 master's graduates. His advising emphasizes agricultural robotics applications, reflected in student projects on robotic pruning, sensor calibration, and autonomous harvesting systems. He directs the Kantor Lab and contributes to AIIRA, developing systems like Bumblebee (autonomous vine pruning), Hefty (modular agricultural robot), and T-REX (vision-based leaf detection). Current work focuses on robotic manipulation in unstructured agricultural environments, sensor exchange for crop monitoring, and AI-driven solutions for resilient food systems.

Yaser Ajmal Sheikhمشاهده پروفایل

دانشیار

• Computer Vision
• 3-D Vision and Recognition
• Human Behavior Analysis
• +۸ مورد دیگر

Yaser Ajmal Sheikh is an Associate Professor at the Robotics Institute of Carnegie Mellon University (currently on leave) and serves as Director of Facebook Reality Lab in Pittsburgh. His work focuses on "metric telepresence": remote interactions in AR/VR that are indistinguishable from reality. He has made significant contributions to machine perception and rendering of social behavior, spanning computer vision, computer graphics, and machine learning. Dr. Sheikh's research encompasses analyzing dynamism in scenes from moving cameras, with particular focus on dynamic motion reconstruction, human behavior analysis, estimation of nonrigid motion, and modeling moving cameras in spacetime. His work has led to breakthroughs in social robotics, 3D vision and recognition, and multisensor data fusion. He founded and directs the Facebook Reality Lab in Pittsburgh, continuing his work on human-centered robotics and social robots. His publication record demonstrates consistent innovation in computer vision and graphics, with emphasis on human motion capture, facial animation, and social interaction modeling. His most recent work focuses on neural approaches to facial animation, full-body avatars, and capturing the complex dynamics of human interaction. The research shows a clear trajectory from foundational work in motion capture to sophisticated neural approaches for realistic human representation in virtual environments. Popular Science's Best of What's New Award Honda Initiation Award (2010) Best paper awards at WACV (2012), SAP (2012), SCA (2010), ICCV THEMIS (2009) First place in the MSCOCO Keypoint Challenge (2016) Hillman Fellowship for Excellence in Computer Science Research (2004) Dr. Sheikh has advised numerous doctoral and master's students who have gone on to prestigious positions in academia and industry. His research has been generously supported by government agencies including NSF and DARPA, as well as industrial partners such as Intel, Disney, Nissan, Honda, Toyota, and Samsung. He has served on senior committees at major conferences including SIGGRAPH, CVPR, ICRA, and ICCP, and was an Associate Editor of CVIU. He leads the Panoptic Studio project, a massively multiview system for social motion capture that has produced the CMU Panoptic Studio Dataset. His work on OpenPose has become a standard tool in the field for real-time 2D hand, body, and face keypoint detection. His current research at Facebook Reality Lab continues to push the boundaries of what's possible in virtual and augmented reality through advanced computer vision techniques.

Jessica Zhangمشاهده پروفایل

استاد

• Computational Geometry
• Isogeometric Analysis
• Finite Element Method
• +۱۲ مورد دیگر

Dr. Jessica Zhang is the George Tallman Ladd and Florence Barrett Ladd Professor of Mechanical Engineering at Carnegie Mellon University, with a courtesy appointment in Biomedical Engineering. She holds prestigious fellowships from ASME, SIAM, and other institutions. Her research focuses on computational geometry, isogeometric analysis, and applications in biomedicine and materials science. She has published over 200 technical articles and received numerous awards, including the PECASE and ASME Van C. Mow Medal. Education: PhD in Computational Engineering and Sciences from the University of Texas at Austin (2005), M.Eng. in Aerospace Engineering (UT Austin), and degrees from Tsinghua University in China. Research Interests: Computational geometry, mesh generation, finite element methods, and their applications in biomedical engineering, materials science, and mechanical engineering. Her lab develops algorithms for biomodeling at molecular to organ scales. Recent Achievements: In 2025, she was selected for the AWM-SIAM Kovalevsky Lecture and the ASME Van C. Mow Medal. Her work on neuron growth modeling and machine learning frameworks has advanced computational biomedical applications. Awards: Includes Simons Visiting Professorship, NSF CAREER Award, and multiple best paper awards. She chairs committees for conferences like Solid and Physical Modeling Symposium. Grants and Mentorship: Advised over 50 students and postdocs. Her lab collaborates across disciplines, including NextManufacturing Center and Materials Research Science and Engineering Center. Labs/Teams: Computational Bio-Modeling Lab, focusing on interdisciplinary projects in mechanical, biomedical, and materials engineering. Active in initiatives like Bioengineered Organs and NextManufacturing.

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +۷ مورد دیگر

Eric Poe Xing is a Professor at Carnegie Mellon University's School of Computer Science, holding joint affiliations with the Machine Learning Department, Language Technology Institute, and Computer Science Department. He also serves as President of the Mohamed bin Zayed University of Artificial Intelligence. His research focuses on machine learning methodology, statistical systems, and large-scale computational architectures, with recent work on foundation models for biology (AIDO), world/agent models (PAN), and open-source LLM initiatives (LLM360). He advises numerous students and postdocs in areas like AI, NLP, and computational biology. He teaches graduate courses in Machine Learning and Probabilistic Graphical Models, and actively contributes to academic leadership roles, including ICML program chairs and editorial boards. Research interests span automated reasoning, AI ethics, and scalable computing. His lab, SAILING, develops cutting-edge models for vision-language tasks, bioinformatics, and multi-modal learning. Notable projects include LLM360's open-source AGI efforts and innovations in distributed ML systems. His work emphasizes interdisciplinary applications, from healthcare decision-support (PetuumMed) to climate modeling (ClimSatDiff).